The Big Data universe revolves around this seemingly new role called “data scientist.” For IT professionals who are just now beginning to explore Big Data, the notion of a data scientist may seem a bit trendy, hence suspect. How does it differ from such familiar jobs as statistical analyst, data miner, predictive modeler and content analytics specialist?

Yes, data scientist is a trendy new job title to emboss on your business card. But it’s also a very useful new term for referring to a wide range of advanced analytics functions that heretofore have had no consensus category label. The term recognizes that advanced analytics developers, like scientists generally, spend their careers exploring new data for powerful insights that may not be obvious on first glance.

Indeed, one might define a data scientist as someone who uses statistical algorithms and interactive exploration tools to uncover non-obvious patterns in observational data. This definition is broad enough to encompass a wide range of data scientists doing various types of analyses against many data types. The tools may be usable by any intelligent person, or they may be so specialized and abstruse that you practically need a Ph.D. in higher mathematics to get started. The underlying algorithms may be limited to the most common multivariate regression approaches, or may include the latest advances in artificial intelligence and machine learning. The exploration may be highly visual, or it may also involve trial-and-error iteration through complex statistical models.

But don’t fool yourselves into thinking data scientists must live in ivory towers. In fact, far more data scientists work in the business world than in the halls of academe or think tanks. Data scientists can prove to be one of your most strategic assets in the competitive wars.

If you can uncover new patterns in customer sentiment before your rivals, then you can address them before the competition could even cobble together a clue. If you can adjust the media mix behind your digital marketing campaign in real time to ensure the right messages get to the right audiences, you can save the campaign, and possibly your company, from going down the tubes. And by iterating through behavioral analytics models on your customer experience platform, you can conduct “real-world experiments” that help strengthen satisfaction and retention.

Data scientists can be your core brain trust driving these and other applications of advanced analytics.